Ecosystem Modeling in Paleoecology

Yao Liu
18 August 2014

Outline

  • Ecosystem modeling 101: overview and terminology
  • Classes of forest models
    • Submodels: ecophysiology, biogeochemistry, land surface, forest gap
    • Scales
  • Data Requirements & Parameterization
  • Paleo modeling examples
  • PalEON modeling (if time allows)
    • Validation
    • Initialization
    • Inference and improvement (data assimilation)

Why ecosystem modeling

  1. Ecological theory: To codify what we know (or what we believe we know, or what we want to explore), be explicit with our assumptions and logic. Formalizing the knowledge, put it to the test, and experiment with it.

  2. Prediction and forecasting to support decision-making: e.g. forest responses to global change

Modeling is an interative process:

  • Knowledge -> Codify Model -> Validation -> Research/Revise -> Knowledge -> Codify Model …

Basic elements of a model

  1. States and state variables

    • State variables: component parts or “observable” attributes
    • Vary with time, and theoretically measurable at any time
    • Example: for “the vegetation of the North Woods Ecosystem”, what are some states and state variables?
  2. Inputs (parameters, meteorological and envrionmental drivers, surface conditions, etc.)

    • More on driving variable: Quantities outside of the system boundaries that affect process within the system. (aka. driving functions, forcing, input variables*).
  3. Outputs or predictions

Classes of forest models

Dietze and Latimer 2012 alt text

Terrestial biosphere models/ Ecosystem models: computer models used to predict the states and dynamics of terrestial ecosystems

What processes should we include in our models?

  • Submodels
  • Spatial Scales
  • Temporal Scales
  • Contrasts

Ecophysiological Models

alt text

  • Conservation of mass
    • Pools and fluxes
    • Carbon, water, N, P
    • Not individual trees
  • Photosynthesis/GPP
    • Enzyme Kinetic, LUE

Ecophysiological Models: Photosynthesis

Enzyme Kinetic (Farquhar model)
\[ A = f(PAR,CO2,RH) \] alt text Long and Bernacchi, 2003

Light use efficiency (LUE)
\[ A = LUE*PAR*M(env) \]

To calculate GPP
\[ GPP = A * LAI \]

Ecophysiological Models cont.

alt text

  • Conservation of mass
    • Pools and fluxes
    • Carbon, water, N, P
    • Not individual trees
  • Photosynthesis/GPP
    • Enzyme Kinetic, LUE
  • Allocation/Allometry

Ecophysiological Models cont.

alt text

  • Conservation of mass
    • Pools and fluxes
    • Carbon, water, N, P
    • Not individual trees
  • Photosynthesis/GPP
    • Enzyme Kinetic, LUE
  • Allocation/Allometry
  • Respiration = f(pool size)
    • \[ NPP = GPP - Ra \]
  • Turnover

Biogeochemical models: Century (carbon)

alt text

Modified by:

  • Temperature
  • Moisture
  • C:N
  • Lignin

Recall that \[ NPP = GPP - Ra \] With BGC model \[ NEP = NPP - Rh \]

Biogeochemical models: TECO

alt text

TECO model components:

  • Plant ecophysiology

  • Soil biogeochemistry

Land Surface Model

alt text

  • Exchange of energy and moisture between land and atmosphere
  • Originally developed for atm models
  • Static vegetation
  • Fast time step

Land Surface Model

alt text

Flux = g * DX

  • g = Conductance
  • DX is concentration gradient
  • Leaf in → out
  • Leaf surf → canopy air space (CAS)
  • Soil → CAS
  • Between soil layers
  • Within snow
  • CAS → ATM

Leaf Energy Balance

alt text

T = Temperature
a = absorptivity
e = emissivity
L = latent heat of vaporization
C = heat capacity
z = solar zenith angle

Forest Gap Models

alt text

  • Individual based
  • Multiple spp or pft within a patch
  • Height structured competition
  • 1D or 3D light
  • Demographic
  • Succession
  • Disturbance

Spatial Scale

  • Individual
    • Spatially explicit
  • Patch
    • ~10-20m or homogeneous stand
  • Landscape
    • Collection of patches, topography, disturbance, land use
  • Region/Globe
    • Coarse grid cells

Temporal Scale

  • Subdaily
    • Land surface
    • Photosynthesis
  • Daily to monthly
    • Allocation, respiration, BGC, phenology
  • Seasonal to interannual
    • Growth, mortality, reproduction
    • Disturbance
    • Dispersal / migration

Linking processes at different scales

alt text

Diagram by Dennis Baldocchi

Contrasts

  • Phenomenologic vs Mechanistic
  • Stochastic vs Deterministic
  • Diagnostic vs Prognostic
  • Point vs Area

Table 1 in Dietze and Latimer (2012) Forest Simulators. In Encyclopedia of Theoretical Ecology. University of California Press.

alt text

Research objectives --> model choice

  • Ecosystem models represent processes differently, at differnt spatial and temporal scales, with different attributes
  • Examples: SIPNET vs Community Land Model (CLM) vs Ecological Demography 2 (ED2)

Data Requirements and parameterization

Data Requirements

  • Meteorology (subdaily or daily)
    • Temperature, precip
    • Humidity, solar radiation, CO2
    • Long wave, wind speed, pressure
  • Initial Conditions
    • Vegetation: biome, PFT, spp
    • Data driven vs spin-up
  • Disturbance/Land Use
  • N deposition

Parameterization

alt text

Parameter values you need to know before you could make predictions using a model like this?

Diagram by Dennis Baldocchi

Parameterization

  • Approaches
    • Literature-based (ad hoc or meta-analysis)
    • Tuning
    • Optimization (single best estimate)
    • Model-data fusion / Data Assimilation (uncertainties)
  • Data
    • Eddy flux (instantaneous to interannual)
    • Forest inventory (annual to decadal)
    • Other ground based measurements (inst. to interann.)
    • Experimental manipulations (FACE, N, precip)
    • Remote sensing (daily to interannual)

Long-term Dynamics

  • Under-constrained
  • Occasionally chronosequence, few paleo
  • Biogeochemistry
    • Slow soil C pools take centuries to equilibrate
  • Gap/Landscape:
    • Succession is decades to centuries
    • Patch mosaic, shifts in disturbance regime

Paleo modeling examples

Data-model comparison: past veg. change

alt text Miller et al. (2008) Exploring climatic and biotic controls on Holocene vegetation change in Fennoscandia. Journal of Ecology

alt text

Migration and vegetation dynamics

alt text Lehsten, Doerte, et al. (2014) “Modelling the Holocene migrational dynamics of Fagus sylvatica L. and Picea abies (L.) H. Karst.” Global Ecology and Biogeography.

Reconstructing terrestial carbon storage

alt text Wu et al. (2009) New coupled model used inversely for reconstructing past terrestrial carbon storage from pollen data: validation of model using modern data. Global Change Biology, 15, 82-96 .

alt text

Reconstructing paleoclim. and paleoveg.

alt text alt text

Garreta et al., 2009 alt text

PalEON Modeling Objectives

PalEON Modeling Objectives

“[L]ike many areas of climate change science, but unlike most areas of ecology, the understanding of biosphere-atmosphere interactions fundamentally relies on the predictions of large, complex models whose parameters are difficult to measure, and that make predictions at scales far larger than we are typically able to make measurements. As a result, the findings of terrestrial biosphere modeling studies are usually appropriately couched in terms of ‘potential feedback mechanisms’. Indeed, a harsh, but not entirely unwarranted, view would be that our current understanding of biosphere-atmosphere feedbacks is a collection of interesting, but largely untested, hypotheses for the future state of terrestrial ecosystems and climate.” .

Moorcroft et al.(2006)

Validation

How well do current models simulate decadal-to-centennial ecosystem dynamics when confronted with past climate change, and what factors most limit model accuracy?

Activities:
1. Pre-settlement “potential vegetation” for the region based on running their model to equilibrium
2. New runs over the PalEON time period under common drivers from a small ensemble of different GCM
3. Compare models at points and to stats group's gridded data product

Initialization

How sensitive are ecosystem models to initialization state and equilibrium assumptions? Do data-constrained simulations of centennial-scale forest dynamics improve 20th-century simulations? How long does initial condition matter?

Inference

What net carbon fluxes are compatible with an observed species composition and disturbance regime? Was the terrestrial biosphere a carbon sink or source during the Little Ice Age and Medieval Climate Anomaly?

With PalEON data products in hand, use data-assimilation to alter the model's state variables and disturbance regime in order to stay “on track” with the observations and take into account the uncertainties in those observations.

Inference

State-variable Data Assimilation:

alt text

Improvement

To what extent does site-level data-model fusion improve regional model projections? Which parameters are most responsible for data-model divergences at decadal to centennial timescales, and how can they be improved? Where can we get most value from future field campaigns?